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Distributed Adaptation in Multi-robot Search Using Particle Swarm Optimization

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From Animals to Animats 10 (SAB 2008)

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Abstract

We present an adaptive strategy for a group of robots engaged in the localization of multiple targets. The robotic search algorithm is inspired by chemotaxis behavior in bacteria, and the algorithmic parameters are updated using a distributed implementation of the Particle Swarm Optimization technique. We explore the efficacy of the adaptation, the impact of using local fitness measurements to improve global fitness, and the effect of different particle neighborhood sizes on performance. The robustness of the approach in non-static environments is tested in a time-varying scenario.

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References

  1. Balch, T.: Behavioral diversity in learning robot teams. PhD Thesis, College of Computing, Georgia Institute of Technology (1998)

    Google Scholar 

  2. Berg, H.C.: E. coli in motion. Springer, NY (2003)

    Google Scholar 

  3. Cianci, C., Raemy, X., Pugh, J., Martinoli, A.: Communication in a swarm of miniature robots: The e-puck as an educational tool for swarm robotics. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) Swarm Rob. Workshop. LNCS, vol. 4433, pp. 103–115. Springer, Heidelberg (2007)

    Google Scholar 

  4. Dhariwal, A., Sukhatme, G.S., Requicha, A.A.G.: Bacterium-inspired robots for environmental monitoring. In: Proc. of the IEEE Intl. Conf. on Robotics and Automation, New Orleans, LA, USA, April 26 - May 1, 2004, pp. 1436–1443 (2004)

    Google Scholar 

  5. Doctor, S., Venayagamoorthy, G.K., Gudise, V.G.: Optimal PSO for Collective Robotic Search Applications. In: Proc. of the IEEE Congress on Evolutionary Computation, Portland, OR, USA, June 19-23, 2004, pp. 1390–1395 (2004)

    Google Scholar 

  6. Goldsmith, S.Y., Robinett, R.: Collective search by mobile robots using alpha-beta coordination. In: Drogoul, A., Tambe, M., Fukuda, T. (eds.) Collective Robotics, pp. 136–146. Springer, Berlin (1998)

    Google Scholar 

  7. Hayes, A.T.: How Many Robots? Group Size and Efficiency in Collective Search Tasks. In: Proc. of the 6th Intl. Symp. on Distributed Autonomous Robotic Systems DARS 2002, pp. 289–298. Fukuoka, Japan (2002)

    Google Scholar 

  8. Hayes, A.T., Martinoli, A., Goodman, R.M.: Swarm Robotic Odor Localization: Off-Line Optimization and Validation with Real Robots. In: McFarland, D. (ed.) Special Issue on Biological Robots. Robotica, vol. 21, pp. 427–441 (2003)

    Google Scholar 

  9. Holland, O., Melhuish, C.: Some adaptive movements of animats with single symmetrical sensors. In: 4th Intl. Conf. on Simulation of Adaptive Behaviour. MIT Press, Cambridge (1996)

    Google Scholar 

  10. Kantor, G., Singh, S., Peterson, R., Rus, D., Das, A., Kumar, V., Pereira, G., Spletzer, J.: Distributed search and rescue with robot and sensor teams. In: Proc. of the 4th Intl. Conf. on Field and Service Robotics, Japan (2003)

    Google Scholar 

  11. Kelly, I.D., Keating, D.A.: Faster learning of control parameters through sharing experiences of autonomous mobile robots. Intl. J. of System Science 29(7), 783–793 (1998)

    Article  MATH  Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization, Neural Networks. In: Proceedings, IEEE Intl. Conf., vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  13. Marques, L., Nunes, U., de Almeida, A.T.: Olfaction-based mobile robot navigation. Thin Solid Films 418, 51–58 (2002)

    Article  Google Scholar 

  14. Matarić, M.J.: Learning in behavior-based multi-robot systems: Policies, models, and other agents. In: Sun, R. (ed.) Special Issue on Multi-disciplinary studies of multi-agent learning. Cognitive Systems Research, vol. 2(1), pp. 81–93 (2001)

    Google Scholar 

  15. Michel, O.: Webots: Professional Mobile Robot Simulation. Int. J. of Advanced Robotic Systems 1, 39–42 (2004)

    Google Scholar 

  16. Ögren, P., Fiorelli, E., Leonard, N.E.: Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment. IEEE Transactions on Automatic Control 49(8), 1292–1302 (2004)

    Article  Google Scholar 

  17. Osuka, K., Murphy, R., Schultz, A.C.: USAR Competitions for Physically Situated Robots. IEEE Robotics and Automation Magazine, 26–33 (September 2002)

    Google Scholar 

  18. Pugh, J., Zhang, Y., Martinoli, A.: Particle swarm optimization for unsupervised robotic learning. In: Swarm Intelligence Symp., Pasadena, CA, pp. 92–99 (2005)

    Google Scholar 

  19. Pugh, J., Martinoli, A.: Multi-Robot Learning with Particle Swarm Optimization. In: Intl. Conf. on Autonomous Agents and Multiagent Systems, Hakodate, Japan, May 8-12, 2006, pp. 441–448 (2006)

    Google Scholar 

  20. Pugh, J., Martinoli, A.: Relative Localization and Communication Module for Small-Scale Multi-Robot Systems. In: Proc. of the IEEE Intl. Conf. on Robotics and Automation, Miami, Florida, USA, May 15-19, 2006, pp. 188–193 (2006)

    Google Scholar 

  21. Stone, P.: Layered Learning in Multi-Agent Systems. PhD Thesis, School of Computer Science, Carnegie Mellon University (1998)

    Google Scholar 

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Minoru Asada John C. T. Hallam Jean-Arcady Meyer Jun Tani

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© 2008 Springer-Verlag Berlin Heidelberg

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Pugh, J., Martinoli, A. (2008). Distributed Adaptation in Multi-robot Search Using Particle Swarm Optimization. In: Asada, M., Hallam, J.C.T., Meyer, JA., Tani, J. (eds) From Animals to Animats 10. SAB 2008. Lecture Notes in Computer Science(), vol 5040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69134-1_39

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  • DOI: https://doi.org/10.1007/978-3-540-69134-1_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69133-4

  • Online ISBN: 978-3-540-69134-1

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